The Endocannabinoid System in the Vervet Monkey Retina
Bibliographic record
Abstract
The main active compound found in the marijuana plant, tetrahydrocannabinol, is responsible for its psychotropic effects but also for its numerous beneficial actions such as appetite stimulation, nausea reduction, analgesia, and muscle spasm suppressor. Although cannabis consumption leads to some visual disturbances, the exact role of the endocannabinoid system (ECS) in normal vision is still unknown. Many studies have looked into the localization of this complex system (receptors, ligands, and enzymes) throughout the various components of the visual system of different animal models in order to obtain clues about its role. In fact, the retina, optic nerve, dorsal lateral geniculate nucleus, and visual cortices all express parts of the ECS. Manipulating this system pharmacologically or genetically has also an impact on visual function. In this book chapter, we provide the current understanding of how the ECS is involved in the functioning of the visual system and special emphasis is put on data obtained in monkeys, representing the most relevant animal model for visual neuroscience research. The mechanisms that control endocannabinoid (eCB) release and activation of cannabinoid receptors are discussed. We also propose a model highlighting the mechanisms involved in the regulation of photopic and scotopic vision taking advantage of the spatial specificity of the eCB signaling system and its physiological activation conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".